1. Field
The invention pertains to enhancing the quality of recorded service data, such as data recorded on service tickets, in a data center or call center.
2. Description of the Related Art
Service delivery centers are large, complex and dynamic ecosystems, which engage hundreds of thousands of experts globally to manage thousands of processes supporting thousands of IT systems with hundreds of configurations. While operations at service delivery centers are typically associated with back-end processes, its efficiency affects quality at front-end (e.g., client experience and satisfaction).
Multiple ticketing systems, data stores and warehouses trace the operations in service delivery centers. They capture practices of Subject Matter Experts (SMEs), who are typically System Administrators (SAs), and changes in the IT infrastructure (e.g., server decommissioning). These ticketing systems, and enterprise-level warehouses are only as reliable as their sources, whether human-driven (tickets submitted by SAs) or system-driven (automated updates of server registries).
All too often, there is poor quality of captured data when managing a data center or call center. Administrators are time pressured to achieve high throughput and problem resolution, and no incentive exists for quality of records and logs when capturing and describing problems and resolutions. Low quality of such data leads to inefficiencies in operations (e.g., incomplete tickets slow down the problem resolution process), or leads business analytics to reach wrong or suboptimal conclusions. Frequently, data records such as tickets are blank with insufficient data, and as such are unusable.
Moreover, low quality of data affects the business decisions (e.g., leading to poor business insights when identifying opportunities for new service offerings, such as “show me the low utilization servers across the banking sector”). Business insights and problem resolution processes require careful quality assessment to build credibility with stakeholders and efficiently resolve problem tickets. Moreover in such volatile environments, quality of operations and business insights will vary depending on the corresponding data source.
Planning activities also depend on good quality data. Take for example server consolidation, where old servers or underutilized servers are migrated into virtual environments with newer hardware. Being able to understand the configuration information such as number of CPUs, speed, memory, operating system and software configured as well as resource information such as network bandwidth, disk and CPU utilization are all key to be able to prepare a plan that maps to proper sized servers. Bad quality data could easily derail a plan from improper source selection to bad target allocations.
Accumulated problem resolution records contain tremendous source of information about the managed system, its efficiencies and weaknesses, and in addition to analytics, it is a valuable source for knowledge transfer and learning in attempt to train new administrators. The record data are also used for reporting and report generation in billing and service level agreement (SLA) measurements.
Accurate records of services provided are valuable for a number of business aspects. These include planning of future system improvements, automating problem resolution, optimization of tasks, and awarding the best administrators and skill development. It would be desirable to have a way to improve capturing of incident and problem description and resolution in a data or call center.